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Characteristics of Acoustic Gravity Waves from the Tonga Volcano Monitored on the Chinese Mainland on January 15, 2022

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Abstract

Based on the barometric data recorded by the seismic monitoring network on the Chinese mainland, Lamb wave periods and relative arrival times of 462 stations are obtained with the aid of Meyer’s wavelet decomposition, Welch’s periodogram spectrum estimation and waveform cross-correlation. By extracting the seismic Rayleigh waves of two seismic stations in the South Pacific and comparing them with the synthetic seismograms, the occurrence time of the first two large volcanic eruptions and the largest volcanic eruption are credibly deduced, and then the travel times and propagation speeds of the Lamb waves are obtained. In order to further explain the attributes of the pressure disturbance associated with the complex acoustic gravity wave (AGW) phases on pressure, a series of numerical simulations with the QSSP program are applied to yield highly similar waves corresponding to the barometric records, which indicates that the eruption source may contain more than 10 sub-events within an hour. According to our detailed analyses, major conclusions are obtained as follows: (1) Although the primary pressure disturbance of the Tonga volcanic eruption appears to be a simple bulge, it is in fact a complex wave composed of multiple eruptions. The largest eruption occurred about 13 min after the first large eruption. (2) In addition to the propagation of the traditional Lamb mode, the Pekeris mode phase that propagates with a lower speed is also observed in some stations, and its amplitude is about a fifth of the Lamb wave. The Pekeris waves may have been more significantly delayed as they traveled against the westerly wind due to their much shorter periods and lower velocities. (3) The group velocity of the Lamb wave is about 308 m/s. Its average period is 70 min, and the wavelength is about 1300 km. The arrival time deviation at each station is negatively correlated with the difference in the near-surface air temperature between North and South China. However, accurately estimating the parameters of the Pekeris waves or the antipodal Lamb waves is challenging due to their low signal-to-noise ratio (SNR), even though the horizontal propagation speed of the Pekeris wave toward China is estimated to be approximately 225 m/s. (4) Much shorter periods and later arrivals at the stations within 200 km of Beijing may be related to the significant cooling (i.e. 12 °C temperature drop), which occurred from Ulantoba, Outer Mongolia, to Beijing beginning at noon on January 15 (Beijing time).

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Data availability

Data of the 2-day pressure records in the paper available on request from the corresponding author.

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Acknowledgements

The authors express special thanks to the China Earthquake Networks Center, CEA for providing all barometric data at the seismic stations on the Chinese mainland. During the writing process of this paper, Professor Liang Jianhong of the China Seismic Network Center provided substantial assistance for us. Ms. Murphy from University of Virginia helped us improve the readability of this paper. Additionally, we would like to show our sincere appreciation to the reviewers providing us with many valuable suggestions and references to improve the original manuscript. Finally, we would like to thank Professor Wang Rongjiang for providing us with the refined programs of seismic wave simulation in 2017.

Funding

The authors declare that there is no funding for supporting our research or achievement of our manuscript.

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Authors and Affiliations

  1. Tianjin Earthquake Agency, Tianjin, 300201, China

    Shuangqing Liu, Song Chen, Huiqin Yao, Dali Jin, Yixi Wang & Yue Li

  2. China Earthquake Networks Center, Beijing, 100045, China

    Yan Xue

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Contributions

SL takes full responsibility for this manuscript, including data processing, establishing mathematical model, numerical simulation, drawing the figures, and designing and writing the manuscript. YX is responsible for the logical examination and refining to the manuscript. SC provides the most of barometric data, and contact relevant staffs working at seismic stations to confirm the reliability of the data. HY accomplishes some data preprocessing and rechecks a series of results produced by a batch process with computer. DJ provides a part of barometric data and supplements the new observation data required by the revised draft. YW and YL accomplish the verification of the previous manuscript and point out some defective drawings, and provide some valuable suggestions.

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Correspondence to Shuangqing Liu.

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Appendix 1

Appendix 1

See Figs.

Fig. 13
figure 13

Example of Meyer wavelet decomposition for pressure data. ai corresponds to the low frequency composition, and di corresponds to the high frequency composition

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13,

Fig. 14
figure 14

Example of extracting the period using the Welch periodogram. The original data of Station Linxikuang have been processed by the baseline offset correction (BOC). Due to the sliding window algorithm, the time of sub-graph (a) has been moved forward by approximately 100 min; the band-pass frequency of extracted data is 2.604e−4–0.0083 Hz

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14,

Fig. 15
figure 15

Speed measurement of the Lamb wave and related residual distribution

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15,

Fig. 16
figure 16

Travel time of the Lamb wave at each station

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16,

Fig. 17
figure 17

Comparison between the near-surface air temperature and travel time deviation

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17,

Fig. 18
figure 18

Comparison between the amplitude of Lamb wave and the background pressure at each station; red spots correspond to the stations encircled in Fig. 12

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18.

1.1 The Main Parameters for Input of the QSEIS Program

Source depth: 0.0–0.2

Receiver_depth: 0.0sw_equidistant/sw_d_unit: 1/0

Start and end trace distance: 6.808 7.482t_start/twindow/no_t_samples: −50.0 1200.0 2048sw_t_reduce/t_reduce:0/6.0sw_algorithm: 1sw_cut_off: 0.0 0.005 0.32 0.35

Accuracy: 4supp_factor: 0.10issurf: 0sw_path_fliter/shallow_depth_limit: 0/400.0no_of_depth_ranges:0wavelet_duration/sw_wavelet: 25.0 (45.0)/1

Mxx/Myy/Mzz/Mxy/Myz/Mzx: 0.15e+18/0.15e+18/0.15e+18/0/0/0 (First Source)

0.63e+18/0.63e+18/0.63e+18/0/0/0 (Second Source)sw_flat_earth_transform: 1vp_res/vs_res/ro_res: 0.25/0.25/0.5

Layered Earth Model is based on IASP91 and physical dispersion according to Kanamori and Anderson (1977), but the shallower depths above the 71 km are modified slightly as follows:

#Multilayered model parameters (source site)

No.

Depth [km]

vp [km/s]

vs [km/s]

ro [g/cm3]

qp

qs

1

0.00

4.800

2.360

2.600

1752.1

778.7

2

3.00

5.800

3.360

2.600

1752.1

778.7

3

20.00

5.800

3.360

2.600

1412.2

627.6

4

20.00

6.500

3.750

2.900

1412.2

627.6

5

30.00

6.500

3.750

2.900

1449.1

601.4

6

30.00

8.040

4.470

3.381

1449.1

601.4

7

71.00

8.044

4.483

3.375

1464.1

607.1

1.2 The Main Parameters for Input of the QSSP Program

Uniform receiver depth [km]:0.0–86.0

Time window [s]/sampling interval [s]: 60,000.0/10.0 max.frequency [Hz] of Green’s functions: 0.05 max.slowness [s/km] of Green’s functions: 4.0

Anti-aliasing factor: 0.1

Switch of turning-point filter/the range (d1, d2) of max. penetration depth [km]: 0/2891.5/6371.0

Earth radius [km]/switch of free-surface-reflection filter: 6371.0/1.

The critical frequency [Hz]/the critical harmonic degree: 0.05/500

Selection of spheroidal modes/selection of toroidal modes/minimum/maximum cutoff harmonic degrees: 1/0/100/10000

Number of discrete source depths/estimated radius of each source patch [km]: 1/0.0

List of the source depths [km]/the respective file names of the Green’s functions/the switch number: 0.2 to −3.0/′ME_A_001km′/1

Number of discrete point sources/selection of the source data format:14/1

List of the multi-event sources

M-Unit/Mrr/Mtt/Mpp/Mrt/Mrp/Mtp/Lat/Lon/Depth/T_origin/T_rise

0.0311e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/0.0 /20.0

0.0606e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/200.0/18.0

0.0799e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/280.0/22.0

0.0187e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/340.0/22.0

0.1370e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/560.0/20.0

0.1693e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/620.0/20.0

0.1122e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/680.0/8.0

0.0125e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/840.0/6.0

0.1495e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/940.0/15.0

0.1370e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/1020.0/10.0

0.1122e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/1080.0/6.0

0.0747e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/1900.0/5.0

0.0436e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/2500.0/5.0

0.0561e+19/1.0/1.0/1.0/0.0/0.0/0.0/0.0/0.0/0.2  to  −3.0/3300.0/4.0

Output time window [s]: 60,000.0

Selection of order of Butterworth bandpass filter (if <  = 0, then no filtering)/lower/upper corner frequencies:0/0.001/0.130

Lower and upper slowness cutoff [s/km]: 0.0/4.0

The multilayered model parameters:

The solid part is based on Prem_1s_178_without ocean

The atmospheric part is based on US Standard Atmosphere (1976) with the top of 86 km height

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Liu, S., Xue, Y., Chen, S. et al. Characteristics of Acoustic Gravity Waves from the Tonga Volcano Monitored on the Chinese Mainland on January 15, 2022. Pure Appl. Geophys. 180, 2487–2509 (2023). https://doi.org/10.1007/s00024-023-03307-w

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